elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets

Schucknecht, Anne und Seo, Bumsuk und Krämer, Alexander und Asam, Sarah und Atzberger, Clement und Kiese, Ralf (2022) Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets. Biogeosciences, 19 (10), Seiten 2699-2727. Copernicus Publications. doi: 10.5194/bg-19-2699-2022. ISSN 1726-4170.

[img] PDF - Verlagsversion (veröffentlichte Fassung)
4MB

Offizielle URL: https://bg.copernicus.org/articles/19/2699/2022/

Kurzfassung

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UAS) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (Linear Model; Random Forests, RF; Gradient Boosting Machines, GBM) and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors, but was not available in our study. Therefore, we tested the added value of this structural information with in-situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in Southern Germany to obtain in-situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized and all model set-ups were run with a six-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor-predictor set combinations with average (avg) R2cv of 0.48, RMSEcv, avg of 53.0 g m2 and rRMSEcv, avg of 15.9 % for DM, and with R2cv, avg of 0.40, RMSEcv, avg of 0.48 wt.% and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R2cv = 0.67, RMSEcv = 41.9 g m2, rRMSEcv = 12.6 %) was achieved with a RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of a RF model with all predictors and SEQ sensor data (R2cv = 0.47, RMSEcv = 0.45 wt.%, rRMSEcv = 14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating ML algorithm improved the model performance substantially, which shows the importance of this step.

elib-URL des Eintrags:https://elib.dlr.de/186693/
Dokumentart:Zeitschriftenbeitrag
Titel:Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Schucknecht, Anneanne.schucknecht (at) kit.eduNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Seo, BumsukKIT Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Krämer, Alexanderalexander.kraemer (at) wwl-web.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Asam, Sarahsarah.asam (at) dlr.dehttps://orcid.org/0000-0002-7302-6813NICHT SPEZIFIZIERT
Atzberger, ClementBoKu WienNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kiese, RalfKIT Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:1 Juni 2022
Erschienen in:Biogeosciences
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:19
DOI:10.5194/bg-19-2699-2022
Seitenbereich:Seiten 2699-2727
Verlag:Copernicus Publications
ISSN:1726-4170
Status:veröffentlicht
Stichwörter:grassland, biomass, nitrogen, UAV, alpine,multispectral, machine learning
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Hinterlegt von: Asam, Dr. Sarah
Hinterlegt am:08 Jun 2022 10:10
Letzte Änderung:08 Jun 2022 10:10

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.